The chosen baselines belong to different clustering approaches which cover a wide range of alternative viewpoints for clustering, e.g., those based on a cost function, probabilistic methods, game-theoretic methods and spectral methods. With the GMM method, we obtain the probabilistic assignment of the objects to the clusters. Then, we assign each object to the most probable cluster.
The developed clustering perspective can potentially be combined with the recent developments proposed in particular for cost-based clustering methods. For example, a category of recent clustering methods aim to combine deep representation learning methods with clustering [1]}, [2]}, or develop approximate and distributed clustering methods. Such contributions are orthogonal to our contribution and, in principle, can be combined with Shifted Min Cut as well. On the other hand, considering the relation between Shifted Min Cut and Correlation Clustering, with the co-authors, we have recently [3]} studied the performance of the local search optimization compared to a wide range of approximate methods developed for Correlation Clustering and have demonstrated both efficiency and effectiveness for the local search method.
